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Application of Machine Learning in Understanding Sentiment of a Conversation

- Pre-processing
- Training
- Punctuation removal
- Tokenization
- Stopword removal
- Lemmatization
- Converting to n-grams
CONVERSATION | USER CONVERSATION ANALYSIS |
AGENT: Hello, How may I assist you? USER: The mobile phone that I had bought from your store Is not working fine AGENT: As I can see in my system that you ordered a phone two days ago which got delivered yesterday. I assure you for the best service. USER: Yeah, I need a refund for this defective product AGENT: Don't worry, we have escalated the matter. A pickup agent will pick the phone and the refund for the same will be processed. USER: Thank you so much for the quick resolution. AGENT: We are here to help you. Have a great day ahead. | { "topic_id": 140452656574016, "tags": ["working","store","mobile","bought","fine","phone"], "sentiment": "negative", "created_on": 1541116800, "raw_conversation": "The mobile phone that bought from your store Is not working fine" } { "topic_id": 140452656574016, "tags": [ "refund","need","product","defective"], "sentiment": "negative", "created_on": 1541116800, "raw_conversation": "I need a refund for this defective product" } { "topic_id": 140452656574016, "tags": [ "quick","much","resolution","Thank"], "sentiment": "positive", "created_on": 1541116800, "raw_conversation": "Thank you so much for quick resolution." } |
- Enhanced searching with fuzziness - The main role of Elasticsearch in the tag cloud is to enhance the searching mechanism and provide the stats on the analytics page in near to real-time. Also, it helps to identify the entities which can be generic entities (date, time, place) or it can be user-defined entities (capturing of shorthand words and converting it to its full form). Fuzziness helps to identify correct keywords which are incorrect. (eg. delli -> Delhi).
- As a Datastore - It acts as a data store storing the sentiment as well as pre-processed data inputted to the module. Benefits of saving the pre-processed data help to perform tag based searching and faster aggregation query execution.
- It doesn't take sentence semantics into consideration, therefore, making a decision based on the keywords having part of speech as an adjective.
- It needs a lot of conditions to handle negation, adverbs as well as query containing keywords such as but/else/otherwise.